In this paper, we provide a modern synthesis of the classic inverse compositional algorithm for dense image alignment. We first discuss the assumptions made by this well-established technique, and subsequently propose to relax these assumptions by incorporating data-driven priors into this model. More specifically, we unroll a robust version of the inverse compositional algorithm and replace multiple components of this algorithm using more expressive models whose parameters we train in an end-to-end fashion from data. Our experiments on several challenging 3D rigid motion estimation tasks demonstrate the advantages of combining optimization with learning-based techniques, outperforming the classic inverse compositional algorithm as well as data-driven image-to-pose regression approaches.

This paper extends the popular task of multi-object tracking to multi-object tracking and segmentation (MOTS). Towards this goal, we create dense pixel-level annotations for two existing tracking datasets using a semi-automatic annotation procedure. Our new annotations comprise 65,213 pixel masks for 977 distinct objects (cars and pedestrians) in 10,870 video frames. For evaluation, we extend existing multi-object tracking metrics to this new task. Moreover, we propose a new baseline method which jointly addresses detection, tracking, and segmentation with a single convolutional network. We demonstrate the value of our datasets by achieving improvements in performance when training on MOTS annotations. We believe that our datasets, metrics and baseline will become a valuable resource towards developing multi-object tracking approaches that go beyond 2D bounding boxes.

Despite significant progress in image-based 3D scene flow estimation, the performance of such approaches has not yet reached the fidelity required by many applications. Simultaneously, these applications are often not restricted to image-based estimation: laser scanners provide a popular alternative to traditional cameras, for example in the context of self-driving cars, as they directly yield a 3D point cloud. In this paper, we propose to estimate 3D motion from such unstructured point clouds using a deep neural network. In a single forward pass, our model jointly predicts 3D scene flow as well as the 3D bounding box and rigid body motion of objects in the scene. While the prospect of estimating 3D scene flow from unstructured point clouds is promising, it is also a challenging task. We show that the traditional global representation of rigid body motion prohibits inference by CNNs, and propose a translation equivariant representation to circumvent this problem. For training our deep network, a large dataset is required. Because of this, we augment real scans from KITTI with virtual objects, realistically modeling occlusions and simulating sensor noise. A thorough comparison with classic and learning-based techniques highlights the robustness of the proposed approach.

Given a set of input views, multi-view stereopsis techniques estimate depth maps to represent the 3D reconstruction of the scene; these are fused into a single, consistent, reconstruction -- most often a point cloud. In this work we propose to learn an auto-regressive depth refinement directly from data. While deep learning has improved the accuracy and speed of depth estimation significantly, learned MVS techniques remain limited to the planesweeping paradigm. We refine a set of input depth maps by successively reprojecting information from neighbouring views to leverage multi-view constraints. Compared to learning-based volumetric fusion techniques, an image-based representation allows significantly more detailed reconstructions; compared to traditional point-based techniques, our method learns noise suppression and surface completion in a data-driven fashion. Due to the limited availability of high-quality reconstruction datasets with ground truth, we introduce two novel synthetic datasets to (pre-)train our network. Our approach is able to improve both the output depth maps and the reconstructed point cloud, for both learned and traditional depth estimation front-ends, on both synthetic and real data.

We propose a technique for depth estimation with a monocular structured-light camera, \ie, a calibrated stereo set-up with one camera and one laser projector. Instead of formulating the depth estimation via a correspondence search problem, we show that a simple convolutional architecture is sufficient for high-quality disparity estimates in this setting. As accurate ground-truth is hard to obtain, we train our model in a self-supervised fashion with a combination of photometric and geometric losses. Further, we demonstrate that the projected pattern of the structured light sensor can be reliably separated from the ambient information. This can then be used to improve depth boundaries in a weakly supervised fashion by modeling the joint statistics of image and depth edges. The model trained in this fashion compares favorably to the state-of-the-art on challenging synthetic and real-world datasets. In addition, we contribute a novel simulator, which allows to benchmark active depth prediction algorithms in controlled conditions.

Abstracting complex 3D shapes with parsimonious part-based representations has been a long standing goal in computer vision. This paper presents a learning-based solution to this problem which goes beyond the traditional 3D cuboid representation by exploiting superquadrics as atomic elements. We demonstrate that superquadrics lead to more expressive 3D scene parses while being easier to learn than 3D cuboid representations. Moreover, we provide an analytical solution to the Chamfer loss which avoids the need for computational expensive reinforcement learning or iterative prediction. Our model learns to parse 3D objects into consistent superquadric representations without supervision. Results on various ShapeNet categories as well as the SURREAL human body dataset demonstrate the flexibility of our model in capturing fine details and complex poses that could not have been modelled using cuboids.

With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity. However, unlike for images, in 3D there is no canonical representation which is both computationally and memory efficient yet allows for representing high-resolution geometry of arbitrary topology. Many of the state-of-the-art learning-based 3D reconstruction approaches can hence only represent very coarse 3D geometry or are limited to a restricted domain. In this paper, we propose Occupancy Networks, a new representation for learning-based 3D reconstruction methods. Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. In contrast to existing approaches, our representation encodes a description of the 3D output at infinite resolution without excessive memory footprint. We validate that our representation can efficiently encode 3D structure and can be inferred from various kinds of input. Our experiments demonstrate competitive results, both qualitatively and quantitatively, for the challenging tasks of 3D reconstruction from single images, noisy point clouds and coarse discrete voxel grids. We believe that occupancy networks will become a useful tool in a wide variety of learning-based 3D tasks.

Abstract Peritrichously flagellated Escherichia coli swim back and forth by wrapping their flagella together in a helical bundle. However, other monotrichous bacteria cannot swim back and forth with a single flagellum and planar wave propagation. Quantifying this observation, a magnetically driven soft two‐tailed microrobot capable of reversing its swimming direction without making a U‐turn trajectory or actively modifying the direction of wave propagation is designed and developed. The microrobot contains magnetic microparticles within the polymer matrix of its head and consists of two collinear, unequal, and opposite ultrathin tails. It is driven and steered using a uniform magnetic field along the direction of motion with a sinusoidally varying orthogonal component. Distinct reversal frequencies that enable selective and independent excitation of the first or the second tail of the microrobot based on their tail length ratio are found. While the first tail provides a propulsive force below one of the reversal frequencies, the second is almost passive, and the net propulsive force achieves flagellated motion along one direction. On the other hand, the second tail achieves flagellated propulsion along the opposite direction above the reversal frequency.

A design methodology is presented for creating custom complex magnetic springs through the design of force-displacement curves. This methodology results in a magnet configuration, which will produce a desired force-displacement relationship. Initially, the problem is formulated and solved as a system of linear equations. Then, given the limited likelihood of a single solution being feasibly manufactured, key parameters of the solution are extracted and varied to create a family of solutions. Finally, these solutions are refined using numerical optimization. Given the properties of magnets, this methodology can create any well-defined function of force versus displacement and is model-independent. To demonstrate this flexibility, a number of example magnetic springs are designed; one of which, designed for use in a jumping-gliding robot's shape memory alloy actuated clutch, is manufactured and experimentally characterized. Due to the scaling of magnetic forces, the displacement region which these magnetic springs are most applicable is that of millimeters and below. However, this region is well situated for miniature robots and smart material actuators, where a tailored magnetic spring, designed to compliment a component, can enhance its performance while adding new functionality. The methodology is also expendable to variable interactions and multi-dimensional magnetic field design.

Most of the top performing action recognition methods use optical flow as a "black box" input. Here we take a deeper look at the combination of flow and action recognition, and investigate why optical flow is helpful, what makes a flow method good for action recognition, and how we can make it better. In particular, we investigate the impact of different flow algorithms and input transformations to better understand how these affect a state-of-the-art action recognition method. Furthermore, we fine tune two neural-network flow methods end-to-end on the most widely used action recognition dataset (UCF101). Based on these experiments, we make the following five observations: 1) optical flow is useful for action recognition because it is invariant to appearance, 2) optical flow methods are optimized to minimize end-point-error (EPE), but the EPE of current methods is not well correlated with action recognition performance, 3) for the flow methods tested, accuracy at boundaries and at small displacements is most correlated with action recognition performance, 4) training optical flow to minimize classification error instead of minimizing EPE improves recognition performance, and 5) optical flow learned for the task of action recognition differs from traditional optical flow especially inside the human body and at the boundary of the body. These observations may encourage optical flow researchers to look beyond EPE as a goal and guide action recognition researchers to seek better motion cues, leading to a tighter integration of the optical flow and action recognition communities.

In International Conference on Intelligent Robots and Systems (IROS) 2018, International Conference on Intelligent Robots and Systems, October 2018 (inproceedings)

Abstract

We present a visual odometry (VO) algorithm for a multi-camera system and robust operation in challenging environments. Our algorithm consists of a pose tracker and a local mapper. The tracker estimates the current pose by minimizing photometric errors between the most recent keyframe and the current frame. The mapper initializes the depths of all sampled feature points using plane-sweeping stereo. To reduce pose drift, a sliding window optimizer is used to refine poses and structure jointly. Our formulation is flexible enough to support an arbitrary number of stereo cameras. We evaluate our algorithm thoroughly on five datasets. The datasets were captured in different conditions: daytime, night-time with near-infrared (NIR) illumination and night-time without NIR illumination. Experimental results show that a multi-camera setup makes the VO more robust to challenging environments, especially night-time conditions, in which a single stereo configuration fails easily due to the lack of features.

We present a novel semantic 3D reconstruction framework which embeds variational regularization into a neural network. Our network performs a fixed number of unrolled multi-scale optimization iterations with shared interaction weights. In contrast to existing variational methods for semantic 3D reconstruction, our model is end-to-end trainable and captures more complex dependencies between the semantic labels and the 3D geometry. Compared to previous learning-based approaches to 3D reconstruction, we integrate powerful long-range dependencies using variational coarse-to-fine optimization. As a result, our network architecture requires only a moderate number of parameters while keeping a high level of expressiveness which enables learning from very little data. Experiments on real and synthetic datasets demonstrate that our network achieves higher accuracy compared to a purely variational approach while at the same time requiring two orders of magnitude less iterations to converge. Moreover, our approach handles ten times more semantic class labels using the same computational resources.

European Conference on Computer Vision (ECCV), September 2018 (conference)

Abstract

Omnidirectional cameras offer great benefits over classical cameras wherever a wide field of view is essential, such as in virtual reality applications or in autonomous robots. Unfortunately, standard convolutional neural networks are not well suited for this scenario as the natural projection surface is a sphere which cannot be unwrapped to a plane without introducing significant distortions, particularly in the polar regions. In this work, we present SphereNet, a novel deep learning framework which encodes invariance against such distortions explicitly into convolutional neural networks. Towards this goal, SphereNet adapts the sampling locations of the convolutional filters, effectively reversing distortions, and wraps the filters around the sphere. By building on regular convolutions, SphereNet enables the transfer of existing perspective convolutional neural network models to the omnidirectional case. We demonstrate the effectiveness of our method on the tasks of image classification and object detection, exploiting two newly created semi-synthetic and real-world omnidirectional datasets.

Collective control of mobile microrobotic swarms is indispensable for their potential high-impact applications in targeted drug delivery, medical diagnostics, parallel micromanipulation, and environmental sensing and remediation. Lack of on-board computational and sensing capabilities in current microrobotic systems necessitates use of physical interactions among individual microrobots for local physical communication and cooperation. Here, we show that mobile microrobotic swarms with well-defined collective behavior can be designed by engineering magnetic interactions among individual units. Microrobots, consisting of a linear chain of self-assembled magnetic microparticles, locomote on surfaces in response to a precessing magnetic field. Control over the direction of precessing magnetic field allows engineering attractive and repulsive interactions among microrobots and, thus, collective order with well-defined spatial organization and parallel operation over macroscale distances (~ 1 cm). These microrobotic swarms can be guided through confined spaces, while preserving microrobot morphology and function. These swarms can further achieve directional transport of large cargoes on surfaces and small cargoes in bulk fluids. Described design approach, exploiting physical interactions among individual robots, enables facile and rapid formation of self-organized and reconfigurable microrobotic swarms with programmable collective order.

Miniaturization of interventional medical devices can leverage minimally invasive technologies by enabling operational resolution at cellular length scales with high precision and repeatability. Untethered micron-scale mobile robots can realize this by navigating and performing in hard-to-reach, confined and delicate inner body sites. However, such a complex task requires an integrated design and engineering strategy, where powering, control, environmental sensing, medical functionality and biodegradability need to be considered altogether. The present study reports a hydrogel-based, biodegradable microrobotic swimmer, which is responsive to the changes in its microenvironment for theranostic cargo delivery and release tasks. We design a double-helical magnetic microswimmer of 20 micrometers length, which is 3D-printed with complex geometrical and compositional features. At normal physiological concentrations, matrix metalloproteinase-2 (MMP-2) enzyme can entirely degrade the microswimmer body in 118 h to solubilized non-toxic products. The microswimmer can respond to the pathological concentrations of MMP-2 by swelling and thereby accelerating the release kinetics of the drug payload. Anti-ErbB 2 antibody-tagged magnetic nanoparticles released from the degraded microswimmers serve for targeted labeling of SKBR3 breast cancer cells to realize the potential of medical imaging of local tissue sites following the therapeutic intervention. These results represent a leap forward toward clinical medical microrobots that are capable of sensing, responding to the local pathological information, and performing specific therapeutic and diagnostic tasks as orderly executed operations using their smart composite material architectures.

Journal of Experimental Biology, The Company of Biologists Ltd, June 2018 (article)

Abstract

Many ants use a combination of cues for orientation but how do ants find their way when all external cues are suppressed? Do they walk in a random way or are their movements spatially oriented? Here we show for the first time that leaf-cutting ants (Acromyrmex lundii) have an innate preference of turning counter-clockwise (left) when external cues are precluded. We demonstrated this by allowing individual ants to run freely on the water surface of a newly-developed treadmill. The surface tension supported medium-sized workers but effectively prevented ants from reaching the wall of the vessel, important to avoid wall-following behaviour (thigmotaxis). Most ants ran for minutes on the spot but also slowly turned counter-clockwise in the absence of visual cues. Reconstructing the effectively walked path revealed a looping pattern which could be interpreted as a search strategy. A similar turning bias was shown for groups of ants in a symmetrical Y-maze where twice as many ants chose the left branch in the absence of optical cues. Wall-following behaviour was tested by inserting a coiled tube before the Y-fork. When ants traversed a left-coiled tube, more ants chose the left box and vice versa. Adding visual cues in form of vertical black strips either outside the treadmill or on one branch of the Y-maze led to oriented walks towards the strips. It is suggested that both, the turning bias and the wall-following are employed as search strategies for an unknown environment which can be overridden by visual cues.

Bacteria-driven biohybrid microswimmers (bacteriabots) combine synthetic cargo with motile living bacteria that enable propulsion and steering. Although fabrication and potential use of such bacteriabots have attracted much attention, existing methods of fabrication require an extensive sample preparation that can drastically decrease the viability and motility of bacteria. Moreover, chemotactic behavior of bacteriabots in a liquid medium with chemical gradients has remained largely unclear. To overcome these shortcomings, we designed Escherichia coli to autonomously display biotin on its cell surface via the engineered autotransporter antigen 43 and thus to bind streptavidin-coated cargo. We show that the cargo attachment to these bacteria is greatly enhanced by motility and occurs predominantly at the cell poles, which is greatly beneficial for the fabrication of motile bacteriabots. We further performed a systemic study to understand and optimize the ability of these bacteriabots to follow chemical gradients. We demonstrate that the chemotaxis of bacteriabots is primarily limited by the cargo-dependent reduction of swimming speed and show that the fabrication of bacteriabots using elongated E. coli cells can be used to overcome this limitation.

Developing adaptive materials with geometries that change in response to external stimuli provides fundamental insights into the links between the physical forces involved and the resultant morphologies and creates a foundation for technologically relevant dynamic systems1,2. In particular, reconfigurable surface topography as a means to control interfacial properties 3 has recently been explored using responsive gels 4 , shape-memory polymers 5 , liquid crystals6-8 and hybrid composites9-14, including magnetically active slippery surfaces12-14. However, these designs exhibit a limited range of topographical changes and thus a restricted scope of function. Here we introduce a hierarchical magneto-responsive composite surface, made by infiltrating a ferrofluid into a microstructured matrix (termed ferrofluid-containing liquid-infused porous surfaces, or FLIPS). We demonstrate various topographical reconfigurations at multiple length scales and a broad range of associated emergent behaviours. An applied magnetic-field gradient induces the movement of magnetic nanoparticles suspended in the ferrofluid, which leads to microscale flow of the ferrofluid first above and then within the microstructured surface. This redistribution changes the initially smooth surface of the ferrofluid (which is immobilized by the porous matrix through capillary forces) into various multiscale hierarchical topographies shaped by the size, arrangement and orientation of the confining microstructures in the magnetic field. We analyse the spatial and temporal dynamics of these reconfigurations theoretically and experimentally as a function of the balance between capillary and magnetic pressures15-19 and of the geometric anisotropy of the FLIPS system. Several interesting functions at three different length scales are demonstrated: self-assembly of colloidal particles at the micrometre scale; regulated flow of liquid droplets at the millimetre scale; and switchable adhesion and friction, liquid pumping and removal of biofilms at the centimetre scale. We envision that FLIPS could be used as part of integrated control systems for the manipulation and transport of matter, thermal management, microfluidics and fouling-release materials.

Abstract Soft actuators have demonstrated potential in a range of applications, including soft robotics, artificial muscles, and biomimetic devices. However, the majority of current soft actuators suffer from the lack of real-time sensory feedback, prohibiting their effective sensing and multitask function. Here, a promising strategy is reported to design bilayer electrothermal actuators capable of simultaneous actuation and sensation (i.e., self-sensing actuators), merely through two input electric terminals. Decoupled electrothermal stimulation and strain sensation is achieved by the optimal combination of graphite microparticles and carbon nanotubes (CNTs) in the form of hybrid films. By finely tuning the charge transport properties of hybrid films, the signal-to-noise ratio (SNR) of self-sensing actuators is remarkably enhanced to over 66. As a result, self-sensing actuators can actively track their displacement and distinguish the touch of soft and hard objects.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018, IEEE, International Conference on Robotics and Automation, May 2018 (inproceedings)

Abstract

We present a stereo-based dense mapping algorithm for large-scale dynamic urban environments. In contrast to other existing methods, we simultaneously reconstruct the static background, the moving objects, and the potentially moving but currently stationary objects separately, which is desirable for high-level mobile robotic tasks such as path planning in crowded environments. We use both instance-aware semantic segmentation and sparse scene flow to classify objects as either background, moving, or potentially moving, thereby ensuring that the system is able to model objects with the potential to transition from static to dynamic, such as parked cars. Given camera poses estimated from visual odometry, both the background and the (potentially) moving objects are reconstructed separately by fusing the depth maps computed from the stereo input. In addition to visual odometry, sparse scene flow is also used to estimate the 3D motions of the detected moving objects, in order to reconstruct them accurately. A map pruning technique is further developed to improve reconstruction accuracy and reduce memory consumption, leading to increased scalability. We evaluate our system thoroughly on the well-known KITTI dataset. Our system is capable of running on a PC at approximately 2.5Hz, with the primary bottleneck being the instance-aware semantic segmentation, which is a limitation we hope to address in future work.

We address the problem of 3D shape completion from sparse and noisy point clouds, a fundamental problem in computer vision and robotics. Recent approaches are either data-driven or learning-based: Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations; Learning-based approaches, in contrast, avoid the expensive optimization step by learning to directly predict complete shapes from incomplete observations in a fully-supervised setting. However, full supervision is often not available in practice. In this work, we propose a weakly-supervised learning-based approach to 3D shape completion which neither requires slow optimization nor direct supervision. While we also learn a shape prior on synthetic data, we amortize, i.e., learn, maximum likelihood fitting using deep neural networks resulting in efficient shape completion without sacrificing accuracy. On synthetic benchmarks based on ShapeNet and ModelNet as well as on real robotics data from KITTI and Kinect, we demonstrate that the proposed amortized maximum likelihood approach is able to compete with fully supervised baselines and outperforms data-driven approaches, while requiring less supervision and being significantly faster.

Bacteria-propelled biohybrid microswimmers have recently shown to be able to actively transport and deliver cargos encapsulated into their synthetic constructs to specific regions locally. However, usage of synthetic materials as cargo carriers can result in inferior performance in load-carrying efficiency, biocompatibility, and biodegradability, impeding clinical translation of biohybrid microswimmers. Here, we report construction and external guidance of bacteria-driven microswimmers using red blood cells (RBCs; erythrocytes) as autologous cargo carriers for active and guided drug delivery. Multifunctional biohybrid microswimmers were fabricated by attachment of RBCs [loaded with anticancer doxorubicin drug molecules and superparamagnetic iron oxide nanoparticles (SPIONs)] to bioengineered motile bacteria, Escherichia coli MG1655, via biotin-avidin-biotin binding complex. Autonomous and on-board propulsion of biohybrid microswimmers was provided by bacteria, and their external magnetic guidance was enabled by SPIONs loaded into the RBCs. Furthermore, bacteria-driven RBC microswimmers displayed preserved deformability and attachment stability even after squeezing in microchannels smaller than their sizes, as in the case of bare RBCs. In addition, an on-demand light-activated hyperthermia termination switch was engineered for RBC microswimmers to control bacteria population after operations. RBCs, as biological and autologous cargo carriers in the biohybrid microswimmers, offer notable advantages in stability, deformability, biocompatibility, and biodegradability over synthetic cargo-carrier materials. The biohybrid microswimmer design presented here transforms RBCs from passive cargo carriers into active and guidable cargo carriers toward targeted drug and other cargo delivery applications in medicine.

Soft small robots offer the opportunity to non-invasively access human tissue to perform medical operations and deliver drugs; however, challenges in materials design, biocompatibility and function control remain to be overcome for soft robots to reach the clinic.

The wrinkling and interfacial adhesion mechanics of a gallium-oxide nanofilm encapsulating a liquid-gallium droplet are presented. The native oxide nanofilm provides mechanical stability by preventing the flow of the liquid metal. We show how a crumpled oxide skin a few nanometers thick behaves akin to a highly bendable elastic nanofilm under ambient conditions. Upon compression, a wrinkling instability emerges at the contact interface to relieve the applied stress. As the load is further increased, radial wrinkles evolve, and, eventually, the oxide nanofilm ruptures. The observed wrinkling closely resembles the instability experienced by nanofilms under axisymmetric loading, thus providing further insights into the behaviors of elastic nanofilms. Moreover, the mechanical attributes of the oxide skin enable high surface conformation by exhibiting liquid-like behavior. We measured an adhesion energy of 0.238 ± 0.008 J m–2 between a liquid-gallium droplet and smooth flat glass, which is close to the measurements of thin-sheet nanomaterials such as graphene on silicon dioxide.

Reliable and real-time 3D reconstruction and localization functionality is a crucial prerequisite for the navigation of actively controlled capsule endoscopic robots as an emerging, minimally invasive diagnostic and therapeutic technology for use in the gastrointestinal (GI) tract. In this study, we propose a fully dense, non-rigidly deformable, strictly real-time, intraoperative map fusion approach for actively controlled endoscopic capsule robot applications which combines magnetic and vision-based localization, with non-rigid deformations based frame-to-model map fusion. The performance of the proposed method is demonstrated using four different ex-vivo porcine stomach models. Across different trajectories of varying speed and complexity, and four different endoscopic cameras, the root mean square surface reconstruction errors 1.58 to 2.17 cm.

In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into actively steerable endoscopic capsule robots which will provide more intuitive disease detection, targeted drug delivery and biopsy-like operations in the gastrointestinal(GI) tract. In this study, we introduce a fully unsupervised, real-time odometry and depth learner for monocular endoscopic capsule robots. We establish the supervision by warping view sequences and assigning the re-projection minimization to the loss function, which we adopt in multi-view pose estimation and single-view depth estimation network. Detailed quantitative and qualitative analyses of the proposed framework performed on non-rigidly deformable ex-vivo porcine stomach datasets proves the effectiveness of the method in terms of motion estimation and depth recovery.

Abstract Programming materials with tunable physical and chemical interactions among its components pave the way of generating 3D functional active microsystems with various potential applications in tissue engineering, drug delivery, and soft robotics. Here, the development of a recapitulated fascicle‐like implantable muscle construct by programmed self‐folding of poly(ethylene glycol) diacrylate hydrogels is reported. The system comprises two stacked layers, each with differential swelling degrees, stiffnesses, and thicknesses in 2D, which folds into a 3D tube together. Inside the tubes, muscle cell adhesion and their spatial alignment are controlled. Both skeletal and cardiac muscle cells also exhibit high viability, and cardiac myocytes preserve their contractile function over the course of 7 d. Integration of biological cells with smart, shape‐changing materials could give rise to the development of new cellular constructs for hierarchical tissue assembly, drug testing platforms, and biohybrid actuators that can perform sophisticated tasks.

The effect of redox metals such as iron and copper on multiple sclerosis and amyotrophic lateral sclerosis has been intensively studied. However, the origin of these disorders remains uncertain. This review article critically describes the physiology of redox metals that produce oxidative stress, which in turn leads to cascades of immunomodulatory alteration of neurons in multiple sclerosis and amyotrophic lateral sclerosis. Iron and copper overload has been well established in motor neurons of these diseases' lesions. On the other hand, the role of other metals like cadmium participating indirectly in the redox cascade of neurobiological mechanism is less studied. In the second part of this review, we focus on this less conspicuous correlation between cadmium as an inactive-redox metal and multiple sclerosis and amyotrophic lateral sclerosis, providing novel treatment modalities and approaches as future prospects.

Cancer cells have the capacity to synthesize nanoparticles (NPs). The detailed mechanism of this process is not very well documented. We report the mechanism of biomineralization of aqueous gold chloride into NPs and microplates in the breast-cancer cell line MCF7. Spherical gold NPs are synthesized in these cells in the presence of serum in the culture media by the reduction of HAuCl4. In the absence of serum, the cells exhibit gold microplate formation through seed-mediate growth albeit slower reduction. The structural characteristics of the two types of NPs under different media conditions were confirmed using scanning electron microscopy (SEM); crystallinity and metallic properties were assessed with transmission electron microscopy (TEM) and x-ray photoelectron spectroscopy (XPS). Gold-reducing proteins, related to cell stress initiate the biomineralization of HAuCl4 in cells (under serum free conditions) as confirmed by infrared (IR) spectroscopy. MCF7 cells undergo irreversible replicative senescence when exposed to a high concentration of ionic gold and conversely remain in a dormant reversible quiescent state when exposed to a low gold concentration. The latter cellular state was achievable in the presence of the rho/ROCK inhibitor Y-27632. Proteomic analysis revealed consistent expression of specific proteins under serum and serum-free conditions. A high-throughput proteomic approach to screen gold-reducing proteins and peptide sequences was utilized and validated by quartz crystal microbalance with dissipation (QCM-D). Statement of significance Cancer cells are known to synthesize gold nanoparticles and microstructures, which are promising for bioimaging and other therapeutic applications. However, the detailed mechanism of such biomineralization process is not well understood yet. Herein, we demonstrate that cancer cells exposed to gold ions (grown in serum/serum-free conditions) secrete shock and stress-related proteins with specific gold-binding/reducing polypeptides. Cells undergo reversible senescence and can recover normal physiology when treated with the senescence inhibitor depending on culture condition. The use of mammalian cells as microincubators for synthesis of such particles could have potential influence on their uptake and biocompatibility. This study has important implications for in-situ reduction of ionic gold to anisotropic micro-nanostructures that could be used in-vivo clinical applications and tumor photothermal therapy.

Surface tension gradients induce Marangoni flow, which may be exploited for fluid transport. At the micrometer scale, these surface-driven flows can be more significant than those driven by pressure. By introducing fluid-fluid interfaces on the walls of microfluidic channels, we use surface tension gradients to drive bulk fluid flows. The gradients are specifically induced through thermal energy, exploiting the temperature dependence of a fluid-fluid interface to generate thermocapillary flow. In this report, we provide the design concept for a biocompatible, thermocapillary microchannel capable of being powered by solar irradiation. Using temperature gradients on the order of degrees Celsius per centimeter, we achieve fluid velocities on the order of millimeters per second. Following experimental observations, fluid dynamic models, and numerical simulation, we find that the fluid velocity is linearly proportional to the provided temperature gradient, enabling full control of the fluid flow within the microchannels.

Despite significant progress achieved in the last decade to convert passive capsule endoscopes to actively controllable robots, robotic capsule endoscopy still has some challenges. In particular, a fully dense three-dimensional (3D) map reconstruction of the explored organ remains an unsolved problem. Such a dense map would help doctors detect the locations and sizes of the diseased areas more reliably, resulting in more accurate diagnoses. In this study, we propose a comprehensive medical 3D reconstruction method for endoscopic capsule robots, which is built in a modular fashion including preprocessing, keyframe selection, sparse-then-dense alignment-based pose estimation, bundle fusion, and shading-based 3D reconstruction. A detailed quantitative analysis is performed using a non-rigid esophagus gastroduodenoscopy simulator, four different endoscopic cameras, a magnetically activated soft capsule robot, a sub-millimeter precise optical motion tracker, and a fine-scale 3D optical scanner, whereas qualitative ex-vivo experiments are performed on a porcine pig stomach. To the best of our knowledge, this study is the first complete endoscopic 3D map reconstruction approach containing all of the necessary functionalities for a therapeutically relevant 3D map reconstruction.

A soft two-tailed microrobot in low Reynolds number fluids does not achieve forward locomotion by identical tails regardless to its wiggling frequency. If the tails are nonidentical, zero forward locomotion is also observed at specific oscillation frequencies (which we refer to as the reversal frequencies), as the propulsive forces imparted to the fluid by each tail are almost equal in magnitude and opposite in direction. We find distinct reversal frequencies for the two-tailed microrobots based on their tail length ratio. At these frequencies, the microrobot achieves negligible net displacement under the influence of a periodic magnetic field. This observation allows us to fabricate groups of microrobots with tail length ratio of 1.24 ± 0.11, 1.48 ± 0.08, and 1.71 ± 0.09. We demonstrate selective actuation of microrobots based on prior characterization of their reversal frequencies. We also implement simultaneous flagellar propulsion of two microrobots and show that they can be controlled to swim along the same direction and opposite to each other using common periodic magnetic fields. In addition, independent motion control of two microrobots is achieved toward two different reference positions with average steady-state error of 110.1 ± 91.8 μm and 146.9 ± 105.9 μm.

Abstract Wearable transdermal delivery systems have recently received tremendous attention due to their noninvasive, convenient, and prolonged administration of pharmacological agents. Here, the material prospects, fabrication processes, and drug‐release mechanisms of these types of therapeutic delivery systems are critically reviewed. The latest progress in the development of multifunctional wearable devices capable of closed‐loop sensation and drug delivery is also discussed. This survey reveals that wearable transdermal delivery has already made an impact in diverse healthcare applications, while several grand challenges remain.

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep recurrent convolutional neural networks (RCNNs) for the visual odometry task, where convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories.

Untethered small-scale (from several millimetres down to a few micrometres in all dimensions) robots that can non-invasively access confined, enclosed spaces may enable applications in microfactories such as the construction of tissue scaffolds by robotic assembly1, in bioengineering such as single-cell manipulation and biosensing2, and in healthcare3,4,5,6 such as targeted drug delivery4 and minimally invasive surgery3,5. Existing small-scale robots, however, have very limited mobility because they are unable to negotiate obstacles and changes in texture or material in unstructured environments7,8,9,10,11,12,13. Of these small-scale robots, soft robots have greater potential to realize high mobility via multimodal locomotion, because such machines have higher degrees of freedom than their rigid counterparts14,15,16. Here we demonstrate magneto-elastic soft millimetre-scale robots that can swim inside and on the surface of liquids, climb liquid menisci, roll and walk on solid surfaces, jump over obstacles, and crawl within narrow tunnels. These robots can transit reversibly between different liquid and solid terrains, as well as switch between locomotive modes. They can additionally execute pick-and-place and cargo-release tasks. We also present theoretical models to explain how the robots move. Like the large-scale robots that can be used to study locomotion17, these soft small-scale robots could be used to study soft-bodied locomotion produced by small organisms.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems